The present invention relates generally to a video personalizing system, and more particularly, but not by way of limitation, to a video personalizing system for a viewer's active and passive reaction-based video branching/video template instantiation, adaptation and personalization in soft real-time where the viewer, while watching a video, generates or consumes content (e.g., on social media) that is related to the content of the video.
Conventionally, video branching has been done, personalizing template videos has been explored, instantiating videos in soft real time, on need basis, has been explored in literature, and eye tracking and social media content analytics, both exist independently. However, each of these exists independently and has never been considered for combination.
That is, the conventional techniques are not used in combination and thus the conventional techniques fail to: (1) actively monitor viewer reaction and perception to a given video, over time, for purpose of adapting or personalizing the video as it moves forward; (2) actively monitor the content generated by a given viewer on social media while the video is going on, analyze the mood (based upon physiological parameters) and perception (based upon eye movements and facial expressions) of the viewer, and thereby adapt or personalize the video on the fly using video branching techniques; (3) actively track the (related and other) content read by a given viewer on the web or social media (using eye tracking methods), and analyze the viewer's reaction to reading the content (happy, angry, confused etc.), with respect to the nature of the content (funny, thought-provoking etc.), including content directly about the topic being shown currently in the video; and (4) branch, adapt and personalize the shown video on a continuous basis, from a given set of options or by filling in a video template, on-the-fly, based upon the inferred mood and quantum/degree of reception of the presented content by the viewer.
Thus, there is a technical problem in the conventional techniques that the techniques are incapable of providing a video personalizing system that can branch videos, by creating a video from a given set of video-snippets or by generating video-snippets dynamically in soft real time from given templates, based upon a profile derived using viewer's direct participation (writing) on social media, passively inferred interests/disinterests by their current and general online/digital reading behavior, biological parameters such as mood swing (found using wearables looking into physiological parameters), facial reactions (and their evolutions) and eye movement patterns within the current video with the specific analytics, metrics, and assessment based on the video content and expected outcomes.